US7111002B2 - Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR) - Google Patents
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- US7111002B2 US7111002B2 US10/832,501 US83250104A US7111002B2 US 7111002 B2 US7111002 B2 US 7111002B2 US 83250104 A US83250104 A US 83250104A US 7111002 B2 US7111002 B2 US 7111002B2
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/5838—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using colour
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
- G06F18/2178—Validation; Performance evaluation; Active pattern learning techniques based on feedback of a supervisor
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F18/22—Matching criteria, e.g. proximity measures
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- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F18/24155—Bayesian classification
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Abstract
Description
where α, β, and γ are suitable constants and NR′and NN′are the number of documents in D′R and D′N respectively. In this equation, D′R are those images (i.e., documents) that the user found relevant and D′N are those images that the user did not find relevant.
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- U.S. patent application Ser. No. 09/702,292, entitled “Image Retrieval Systems and Methods with Semantic and Feature Based Relevance Feedback”;
- U.S. patent application Ser. No. 09/702,288, entitled “Semi-Automatic Annotation of Multimedia Objects”.
Introduction
where x=[x1, . . . , xn], ε=[ε(x1), . . . , ε(xn)], and Σ=ε{(x−u)(x−u) T}.
Positive Feedback
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- Feature Normalization: This puts equal emphasis on each component. For ki the normalized vector is ki=└xkil, . . . , xkin
i ┘, where
- Feature Normalization: This puts equal emphasis on each component. For ki the normalized vector is ki=└xkil, . . . , xkin
If xki
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- Initialization: Initialize σki to be null and let εki= ki, nk=1.
- Feedback and Update Parameters: In each cycle of Pk;s retrieval process, suppose there is a positive example set Cp={PP1 . . . PPq}: according to Equation 3 the following is the resulting update procedure:
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- Distance Calculation: For each image Pi in the database, its distance di,k is calculated to the example image Pk using Equation 3 in the retrieval after the feedback. di,k=−gk(Pi) That is, the similarity of each image in the database to be refined query is determined by Equation 3 based on the positive examples.
- Sorting by distance if there is no negative feedback.
Negative Feedback
d i,k =d i,k+Σi=1 l(p p
where pP
where wi is the normalized weight of each feedback images specified by user. Sim(j,Xi +)is the similarity between image j and Xi + in low-level features, where 0≦Sim(j,Xi +)≦1. The larger the value is, the more similar these two images would be.
Implementation Employing the Minimal Distance (MD) Technique
Dis(j)=Min{(1−Sim(j,X i +))|i=1, . . . , N R} (6)
R(j,X i +)=Rank{(1−Sim(j,X i +))|jεM} (7)
where R(j,Xi +)is the rank of image j to ith positive feedback image Xi +, with the smallest rank meaning the most similar.
R(j,Xi +)=Min{R(j,Xi +)|i=1, . . . , NR} (8)
Dis(j)=Min{(1−Sim(j,X i −))|i=1, . . . , N N} (9)
Dis(j,X i +)=2.0−Sim(j,X i +)*{Dis(j)+Sim(X i + ,X j,k −)} (10)
R(j,Xi +)=Rank{Dis(j,Xi +)|jεM} (11)
Methodological Implementation of the Exemplary RFCBIR
Claims (1)
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
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US10/832,501 US7111002B2 (en) | 2001-03-30 | 2004-04-26 | Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR) |
US11/042,456 US7113944B2 (en) | 2001-03-30 | 2005-01-25 | Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR). |
US11/458,057 US7546293B2 (en) | 2001-03-30 | 2006-07-17 | Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR) |
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US09/823,534 US6748398B2 (en) | 2001-03-30 | 2001-03-30 | Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR) |
US10/832,501 US7111002B2 (en) | 2001-03-30 | 2004-04-26 | Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR) |
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US09/823,534 Continuation US6748398B2 (en) | 2001-03-30 | 2001-03-30 | Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR) |
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US11/458,057 Continuation US7546293B2 (en) | 2001-03-30 | 2006-07-17 | Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR) |
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US10/832,501 Expired - Fee Related US7111002B2 (en) | 2001-03-30 | 2004-04-26 | Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR) |
US11/042,456 Expired - Fee Related US7113944B2 (en) | 2001-03-30 | 2005-01-25 | Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR). |
US11/458,057 Expired - Fee Related US7546293B2 (en) | 2001-03-30 | 2006-07-17 | Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR) |
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US11/458,057 Expired - Fee Related US7546293B2 (en) | 2001-03-30 | 2006-07-17 | Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR) |
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US20050131951A1 (en) | 2005-06-16 |
US20020174120A1 (en) | 2002-11-21 |
US20040243541A1 (en) | 2004-12-02 |
US6748398B2 (en) | 2004-06-08 |
US7113944B2 (en) | 2006-09-26 |
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